Street Networks Analysis

1: Introduction

Urban street networks are complex spatial networks that evolve through planning decisions and community organization, and in turn shape human connections and interactions within the city. Street networks as spatial networks, has shown that structural model can provide insight into urban street structure, patterns of urban space and connectivity. Complex network analysis is a useful approach to quantitively research to inform and aid practicing planners.

This notebook is for assessing the complexity of the urban built form, specifically through its street networks. The topology includes complexity in terms of density and connectivity are applied in the study. In the analysis, the urban network in the area around Sydney CBD has been evaluated, the urban streets are regarded as edges, while the intersections are as nodes.

2: Code

This section defines the study area and request data from open street map. The function "graph_from_place" can get the specific suburb from OSM. Then plot the graph to see the basic street network.

Project the study area from lat_long to the UTM zone appropriate for the geographic location. Then calculate the graph area.

Then save the street network as GraphML file into the local disk and reload it.

There could be some unnecessary nodes in the street network. This step is to clear the redundant points to make the process quicker.

If the node in the network is a true endpoint, the code will return boolean "True" to separate them with different color. Then use "simplify" to remove all nodes that are not intersections or dead-ends.

Divided roads are represented by separate centerline edges. The intersection of two divided roads thus creates 4 nodes, representing where each edge intersects a perpendicular edge. These 4 nodes represent a single intersection in the real world. This step is to clean up the intersections and extract their xy coordinates.

This is the final step to prepare the data.

OSMNx library easily analyzes networks and calculates network statistics, including spatial metrics based on geographic area or weighted by distance.

Looking across the data set provides a sense of the Sydney CBD street networks. There are 2138 nodes and 288,010 meters of linear street in the study area. However, some of the data is hard to interpret which requires a further visualisation for better undersanding the street network.

3: Visualise Centrality

Calculate the closeness centrality of nodes and edges. Nodes colored by closeness centrality from lowest (dark) to highest (light) for flows originating from and traveling to nodes within the subset.

Measures of centrality indicate the most important nodes in a network. Closeness centrality represents, for each node, the reciprocal of the sum of the distance from this node to all others in the network. Node rank as more central if they are averagely closer to all other nodes. This implies that the larger the distance between a node and all other nodes, the less “close” this node is to the network as a whole, and vice versa, the less the mean geodesic distance between them, the closer this node to the others.

To see in the graph, the nodes in the central has a higher closeness centrality than other node. However, this analysis only calculate the centrality inside the study area, which means the surrounding nodes may be affected due to this limitation. Furthermore, the data from OSM only includes the street which has a tag of road or street. The inner path of hyde park are excluded from the data source which need further process to increase the accuracy.

Betweenness centrality assesses the importance of a node by evaluating the number of shortest paths that pass through it. The maximum betweenness centrality in a network specifies the proportion of shortest paths that pass through the most important node. Nodes or moves with high betweenness may have considerable influence in a network because they can control the “flow” in the network. This is an indicator of resilience: networks with a high maximum betweenness centrality are more prone to failure or inefficiency should this single choke point fail.

From this graph, we can see the node with the maximum betweenness centrality. It explained why eastern distributor always in heavy traffic.

The betweenness centrality capturesthe series of nodes forming skeletal paths that define the structure of the network, signifying that the most number of shortest paths in the network pass through these nodes.

4: Isochrone Map

Isochrone maps are commonly used to depict areas of equal travel time. This map plot the nodes that can be reached on foot in 5,10,15,20,25 minutes, measuring the walkability of the study area.

Create polygons for the isochrone map.

Create a geometry skeleton of the accessible area. Then allow for a parameterized buffer radius that is then applied to the constructed skeleton to ensure a single, complex polygon object is generated to represent more detailed travel distance.

5: Network Orientations

Compare the spatial orientations of the city street network in Sydney CBD.

Kevin Lynch defined "legible” cities as those whose patterns lend themselves to coherent, organized, recognizable, and comprehensible mental images. These help us organize city space into cognitive maps for wayfinding and a sense of place.

Each of the suburbs is represented by a polar histogram depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets and its length represents the relative frequency of streets with those bearings.

As seen from the plots, Sydney suburbs adhere pretty well to a north-south, east-west grid. Although it features a grid in some neighborhoods like the Paddington or Haymarket and, these grids tend to not be aligned with one another, resulting in irregular competing orientations. Furthermore, these grids are constrained by the rail track in many directions.

6: Conclusion

The street network analysis presented insight for the urban built form and street connectivity for the suburbs around Sydney CBD. Centralityeis of different types can bring out the well-connected and important nodes and edges in a master plan, and could serve city planning, design and management as an analysis methods. Performing the centrality analysis for the different surburb of the city will permit analysis the of how street structure changes in different urban context. In the future, the analysis of centrality will be useful in figure out the relationship between the urban structure of cities, the physical activity in different spatial structure and the management of networks.

References

Boeing, G. (2017). Methods and Measures for Analyzing Complex Street Networks and Urban Form. SSRN Electronic Journal.

Crucitti, P., Latora, V. and Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3).

Ganin, A., Kitsak, M., Marchese, D., Keisler, J., Seager, T. and Linkov, I. (2017). Resilience and efficiency in transportation networks. Science Advances, 3(12), p.e1701079.

Newman, M. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), pp.167-256.

Porta, S., Crucitti, P. and Latora, V. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), pp.853-866.

Strano, E., Viana, M., da Fontoura Costa, L., Cardillo, A., Porta, S. and Latora, V. (2013). Urban Street Networks, a Comparative Analysis of Ten European Cities. Environment and Planning B: Planning and Design, 40(6), pp.1071-1086.